This just might be the most important chart in bitcoin history: And glancing at it reminds me of how much the 2014-2016 bear market sucked.

Bitcoin price extrapolation using log log plot and Jupyter notebooks:

GitHub

GitHub - bg002h/BitcoinPowerLawModelExtrapolation: Junyper notebook with log log monthly price data plot, curve fit, and extrapolation

Junyper notebook with log log monthly price data plot, curve fit, and extrapolation - GitHub - bg002h/BitcoinPowerLawModelExtrapolation: Junyper n...

Free app for iOS to run the Jupyter notebook code:

App Store

Carnets - Jupyter (with scipy) App - App Store

Download Carnets - Jupyter (with scipy) by Nicolas Holzschuch on the App Store. See screenshots, ratings and reviews, user tips, and more apps like...

Just download the csv file (mine is stored in a folder named bitcoin inside the notebook folder) and copy the jupyter-code into your notebook…

Bitcoin price extrapolation using Juno to run Jupyter notebook on iOS, vibecoded via grok…main point: $1M Bitcoin by mid 2033 would be reasonable. And $10M Bitcoin in 20 years would be reasonable.

Want to do your own Bitcoin price power law fitting at home? On iOS? Get data here (see image 3)

Bitcoinity.org

Average all the prices using this formula (iOS numbers) =AVERAGEIF(B2:K2,">0") (see image 4) Then copy the average column and paste as values and strip down the data to just the date and the average price, export as CSV file (see image 2). Then buy Juno for iOS ($30) and paste this script in a new Jupyter notebook (you’ll see output like image 1 after you run it): # Bitcoin Monthly Prices — Linear + Log-Log + Power-Law Fit & 20-Year Projection # Y-axis: major grid at EVERY power of 10 + minor grid at 2×–9× # FIXED: floating-point exponentiation to avoid ValueError on negative powers # Juno notebook compatible — February 2026 import pandas as pd import numpy as np import matplotlib.pyplot as plt from scipy.stats import linregress from matplotlib.ticker import FixedLocator, ScalarFormatter # ── CHANGE THIS TO YOUR ACTUAL FILE PATH ───────────────────────── csv_path = './bitcoin/BitcoinMonthlyPricesD.csv' # ── Load and clean data ─────────────────────────────────────────── try: df = pd.read_csv(csv_path) print("CSV loaded successfully!") print("\nFirst 5 rows:\n", df.head()) print("\nColumns:", list(df.columns)) print("Shape:", df.shape) except FileNotFoundError: print(f"File not found: {csv_path}") print("Tip: Use !pwd or check Files → Juno folder") except Exception as e: print("Error loading CSV:", str(e)) date_column = 'Time' value_column = 'Avg' df[date_column] = pd.to_datetime(df[date_column], format='%m/%d/%y', errors='coerce') df = df.dropna(subset=[date_column]) df = df.sort_values(date_column) x_dates = df[date_column] y_data = df[value_column].astype(float) # Summary print("\nDate range:", x_dates.min().strftime('%Y-%m'), "to", x_dates.max().strftime('%Y-%m')) print("Valid points:", len(y_data)) print(f"Price range: ${y_data.min():,.2f} – ${y_data.max():,.2f}") # ── Plot 1: Linear scale ────────────────────────────────────────── plt.figure(figsize=(11, 6)) plt.scatter(x_dates, y_data, color='blue', alpha=0.7, s=50, edgecolor='none') plt.xlabel('Date') plt.ylabel('Average Price (USD)') plt.title('Bitcoin Monthly Average Price – Linear Scale') plt.grid(True, alpha=0.3) plt.gca().xaxis.set_major_locator(plt.matplotlib.dates.AutoDateLocator(maxticks=12)) plt.gca().xaxis.set_major_formatter(plt.matplotlib.dates.DateFormatter('%b %Y')) plt.xticks(rotation=45, ha='right') plt.tight_layout() plt.show() # ── Prepare data for log-log plot ───────────────────────────────── valid = y_data > 0 y_valid = y_data[valid] x_dates_valid = x_dates[valid] # Years since Bitcoin genesis (2009-01-03) genesis = pd.to_datetime('2009-01-03') years_since = (x_dates_valid - genesis).dt.days / 365.25 years_valid = years_since.astype(float) # Filter to start from ~1 year onward MIN_YEARS=1.0 reasonable = years_valid >= MIN_YEARS years_log = years_valid[reasonable] y_log = y_valid[reasonable] # ── Log-Log Plot + Power-Law Fit + 20-Year Projection ───────────── if len(y_log) < 10: print("Not enough positive data points for fitting (after year 1 filter).") else: log_x = np.log10(years_log) log_y = np.log10(y_log) slope, intercept, r_value, _, _ = linregress(log_x, log_y) B = slope A = 10 ** intercept print("\nPower-law fit: y ≈ A × (years since 2009)^B") print(f" Exponent (B) = {B:.4f}") print(f" Prefactor (A) = {A:.4e}") print(f" R² = {r_value**2:.4f}") print(f" Points used = {len(log_x)} (from year {years_log.min():.1f} onward)")#print(f" Points used = {len(log_x)} (from year ~1 onward)") # ── Create figure ───────────────────────────────────────────── plt.figure(figsize=(12, 7.5)) # Historical data plt.scatter(years_log, y_log, color='darkred', alpha=0.85, s=60, edgecolor='none', label='Historical Data') # Projection years_plot = np.linspace(1.0, 38.0, 800) y_plot = A * years_plot ** B plt.plot(years_plot, y_plot, color='royalblue', lw=2.4, label=f'Fit + projection: y ≈ {A:.2e} × t^{B:.3f} (R² = {r_value**2:.3f})') plt.xscale('log') plt.yscale('log') plt.xlabel('Years since Bitcoin genesis (Jan 2009)') plt.ylabel('Average Price (USD)') plt.title('Bitcoin Monthly Average Price – Log-Log + Power-Law Fit & ~20-Year Projection') # ── EXPLICIT Y-AXIS TICKS & GRID ──────────────────────────────────────────── # Define exponents covering typical BTC price range exponents = np.arange(-2, 10) # 0.01 to 100,000,000 # Major ticks = every power of 10 major_positions = 10.0 ** exponents # use 10.0 to force float exponentiation plt.gca().yaxis.set_major_locator(FixedLocator(major_positions)) # Formatter: plain numbers, no scientific notation, no offset fmt = ScalarFormatter(useOffset=False) fmt.set_scientific(False) plt.gca().yaxis.set_major_formatter(fmt) # Minor ticks = 2× to 9× each decade minor_positions = [] for exp in exponents: for sub in range(2, 10): minor_positions.append(sub * (10.0 ** exp)) # FIXED: 10.0 ** exp plt.gca().yaxis.set_minor_locator(FixedLocator(minor_positions)) # ── Grid lines ────────────────────────────────────────────────────────────── # Major grid: solid at every power of 10 plt.grid(which='major', axis='y', color='gray', linestyle='-', linewidth=1.0, alpha=0.65) # Minor grid: dashed, faint at 2×–9× plt.grid(which='minor', axis='y', color='lightgray', linestyle='--', linewidth=0.5, alpha=0.45) # Keep x-grid subtle plt.grid(which='major', axis='x', color='gray', linestyle='-', linewidth=0.7, alpha=0.5) # Reasonable y-limits to show many decades plt.ylim(0.01, 1e8) # increase to 1e9 or 1e10 if projection goes higher # ── X-axis: starts at 1, log ticks ──────────────────────────── x_tick_values = [1, 2, 3, 5, 7, 10, 15, 20, 25, 30, 35] plt.xticks(x_tick_values, [str(int(y)) for y in x_tick_values]) # Today marker today_years = (pd.to_datetime('today') - genesis).days / 365.25 plt.axvline(today_years, color='gray', ls=':', lw=1.3, alpha=0.65, label=f'Today (~{today_years:.1f} years since 2009)') plt.legend(loc='upper left', fontsize=10.5) plt.tight_layout(pad=1.5) # ── Dual labels on bottom x-axis ────────────────────────────────────────────── # Get current tick locations and "years since" labels current_xticks = x_tick_values years_since_labels = [str(int(y)) for y in current_xticks] # Corresponding calendar years calendar_labels = [f"{2009 + int(y)}" for y in current_xticks] # Create combined labels (two lines per tick) combined_labels = [f"{ys}\n{cal}" for ys, cal in zip(years_since_labels, calendar_labels)] # Apply the two-line labels plt.xticks(current_xticks, combined_labels) # Optional: make the "years since" smaller or italic to distinguish for label in plt.gca().get_xticklabels(): label.set_fontsize(10) # slightly smaller overall # or more advanced: make top line smaller via manual Text objects (harder) plt.show()

I’ve overpaid for bitcoin…like really MASSIVELY overpaid…everyone did at the beginning. I plotted monthly price data since the beginning, did a log-log transform, fit a straight line to the data and noticed when I first bought Bitcoin, I overpaid. This is ironic, given that at the time I genuinely felt like I wasted my money, of which I had very little at the time (first week of residency). And now I know I literally about 10^3 time too much, if you follow the trend line bitcoin price would later establish in reverse. If you delete the data from über early idiots like me, you see the slope of the line substantially increases…and if you delete even more datapoints, everyone who bought the first year and a half overpaid by a considerable amount. It wasn’t until the 22nd month of trading that people started paying a “fair” price. Note that I get a much smaller exponent than most power law folks…huge difference between 4.4 and 5.8, so be careful extrapolating power laws too far into the future.

On call in house today. Reminder, discharging a firearm through your skull is almost always NOT the right answer. While I doubt I’ll ever see such a thing in relationship to bitcoin in my neck of the woods, it is understandable that some people might feel strong negative emotions about the current price of bitcoin. These price gyrations are anything but rare in bitcoin history. The ability to borrow against your bitcoin is much more widely available now than in years past, although terms really suck, IMHO. Avoid borrowing against your stack. And if you must, borrow the least possible and over collateralize 4 or 5 to 1…or sell bitcoin outright and store dollars in the hope of catching a downward bitcoin price gyration…just remember that nobody can predict the future and it’s really hard to put bounds on bitcoin price behavior, even in a probabilistic sense. And if you really want to play the leverage game, segregate your stack from your speculatively held bitcoin. If you blow your leveraged bitcoin, at least you’ll still have a stack left… And if you did get wiped out on a 2:1 bitcoin collateralized loan, now is the time to work harder and stack more to catch up. Everyone should up there stack while the prices are low, and trust me, people already are and these great prices won’t last forever... But don’t hurt yourself over bitcoin price volatility. Expect it. Be ready to enjoy these bitcoin price discount times rather than hate them. It is a symptom of a fiat world that a good deal on bitcoin makes us feel anything other than happy when we don’t need to sell right now…and don’t be too happy when sitting on big paper gains…keep your head down, keep working, keep stacking, and plan out your future expenses and choose wisely how to meet those expenses…sometimes selling bitcoin does make sense in the short term!

I’m grateful that there are weak hands out there holding bitcoin. I took 1.17 bitcoin off the market over the last two days. Someone gave up a retirement size stack for a song. And my fiat mining job will be growing my stack faster than ever…